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qp QAOA maxcut #1735

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0be5889
Refactor demo.py to use 'qp' instead of 'qml'
drdren Apr 6, 2026
e69aebd
Update date of last modification in metadata.json
drdren Apr 6, 2026
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28 changes: 14 additions & 14 deletions demonstrations_v2/tutorial_qaoa_maxcut/demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -130,7 +130,7 @@
version of NumPy.
"""

import pennylane as qml
import pennylane as qp
from pennylane import numpy as np

np.random.seed(42)
Expand All @@ -153,15 +153,15 @@
# unitary operator U_B with parameter beta
def U_B(beta):
for wire in range(n_wires):
qml.RX(2 * beta, wires=wire)
qp.RX(2 * beta, wires=wire)


# unitary operator U_C with parameter gamma
def U_C(gamma):
for edge in graph:
qml.CNOT(wires=edge)
qml.RZ(gamma, wires=edge[1])
qml.CNOT(wires=edge)
qp.CNOT(wires=edge)
qp.RZ(gamma, wires=edge[1])
qp.CNOT(wires=edge)
# Could also do
# IsingZZ(gamma, wires=edge)

Expand All @@ -180,7 +180,7 @@ def bitstring_to_int(bit_string_sample):
# ~~~~~~~
# Next, we create a quantum device with 4 qubits.

dev = qml.device("lightning.qubit", wires=n_wires)
dev = qp.device("lightning.qubit", wires=n_wires)

##############################################################################
# We also require a quantum node which will apply the operators according to the angle parameters,
Expand All @@ -194,23 +194,23 @@ def bitstring_to_int(bit_string_sample):
# by setting ``return_samples=True``.


@qml.set_shots(20)
@qml.qnode(dev)
@qp.set_shots(20)
@qp.qnode(dev)
def circuit(gammas, betas, return_samples=False):
# apply Hadamards to get the n qubit |+> state
for wire in range(n_wires):
qml.Hadamard(wires=wire)
qp.Hadamard(wires=wire)
# p instances of unitary operators
for gamma, beta in zip(gammas, betas):
U_C(gamma)
U_B(beta)

if return_samples:
# sample bitstrings to obtain cuts
return qml.sample()
return qp.sample()
# during the optimization phase we are evaluating the objective using expval
C = qml.sum(*(qml.Z(w1) @ qml.Z(w2) for w1, w2 in graph))
return qml.expval(C)
C = qp.sum(*(qp.Z(w1) @ qp.Z(w2) for w1, w2 in graph))
return qp.expval(C)


def objective(params):
Expand All @@ -237,7 +237,7 @@ def qaoa_maxcut(n_layers=1):
init_params = 0.01 * np.random.rand(2, n_layers, requires_grad=True)

# initialize optimizer: Adagrad works well empirically
opt = qml.AdagradOptimizer(stepsize=0.5)
opt = qp.AdagradOptimizer(stepsize=0.5)

# optimize parameters in objective
params = init_params.copy()
Expand All @@ -248,7 +248,7 @@ def qaoa_maxcut(n_layers=1):
print(f"Objective after step {i+1:3d}: {-objective(params): .7f}")

# sample 100 bitstrings by setting return_samples=True and the QNode shot count to 100
bitstrings = qml.set_shots(circuit, shots=100)(*params, return_samples=True)
bitstrings = qp.set_shots(circuit, shots=100)(*params, return_samples=True)
# convert the samples bitstrings to integers
sampled_ints = [bitstring_to_int(string) for string in bitstrings]

Expand Down
4 changes: 2 additions & 2 deletions demonstrations_v2/tutorial_qaoa_maxcut/metadata.json
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,7 @@
"executable_stable": true,
"executable_latest": true,
"dateOfPublication": "2019-10-11T00:00:00+00:00",
"dateOfLastModification": "2025-10-15T00:00:00+00:00",
"dateOfLastModification": "2026-04-06T00:00:00+00:00",
"categories": [
"Optimization"
],
Expand All @@ -34,4 +34,4 @@
}
],
"discussionForumUrl": "https://discuss.pennylane.ai/t/qaoa-and-optimization/5188"
}
}
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